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OpenBB/examples/usdLiquidityIndex.ipynb
Danglewood 42ba5a67d7 [BugFix] Fix Biztoc term Field Not Working & Fix Streamlit News Dashboard Example (#6745) 
* update for various recent changes to providers and responses

* elif instead of if in biztoc world news term

* fix typo in usdLiquidityIndex
2024-10-07 09:13:58 +00:00

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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculating the USD Liquidity Index with the OpenBB Platform\n",
"\n",
"This popular indicator is made from a simple subtraction of three FRED series that are published every Wednesday, and is often overlayed with risk assets like the S&P 500 Index or Bitcoin. The OpenBB SDK is well suited for this task, let's take a look to create this index.\n",
"\n",
"The formula is defined as:\n",
"\n",
"```console\n",
"WALCL (All Liabilities) – WLRRAL (RRP) – WDTGAL (TGA)\n",
"```\n",
"\n",
"To get these data series, we will use the `openbb-fred` data extension and the `economy` module. First thing is to import the Python interface, and we will also import Pandas to conduct some DataFrame operations."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from openbb import obb\n",
"from pandas import DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are two `fred` functions in the `openbb-economy` router:\n",
"\n",
"- `obb.economy.fred_search()`\n",
"- `obb.economy.fred_series()`"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"OBBject\n",
"\n",
"id: 066c7874-2012-7189-8000-1e79898a8a3c\n",
"results: [{'date': datetime.date(2002, 12, 18), 'WALCL': 719542.0, 'WLRRAL': 21905....\n",
"provider: fred\n",
"warnings: None\n",
"chart: None\n",
"extra: {'results_metadata': {'WALCL': {'title': 'Assets: Total Assets: Total Assets..."
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = obb.economy.fred_series([\"WALCL\", \"WLRRAL\", \"WDTGAL\", \"SP500\"])\n",
"\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is metadata from each series in the warnings of the response object. It can be recovered as a JSON dictionary."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['WALCL', 'WLRRAL', 'WDTGAL', 'SP500'])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'Assets: Total Assets: Total Assets (Less Eliminations from Consolidation): Wednesday Level'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'Millions of U.S. Dollars'"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"metadata = data.extra[\"results_metadata\"]\n",
"\n",
"display(metadata.keys())\n",
"display(metadata[\"WALCL\"].get(\"title\"))\n",
"display(metadata[\"WALCL\"].get(\"units\"))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Querying FRED\n",
"\n",
"If we didn't already know the ID for the series, we can search with:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>series_id</th>\n",
" <th>title</th>\n",
" <th>observation_start</th>\n",
" <th>observation_end</th>\n",
" <th>frequency</th>\n",
" <th>frequency_short</th>\n",
" <th>units</th>\n",
" <th>units_short</th>\n",
" <th>seasonal_adjustment</th>\n",
" <th>seasonal_adjustment_short</th>\n",
" <th>last_updated</th>\n",
" <th>popularity</th>\n",
" <th>group_popularity</th>\n",
" <th>notes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>WALCL</td>\n",
" <td>Assets: Total Assets: Total Assets (Less Elimi...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2024-08-14</td>\n",
" <td>Weekly, As of Wednesday</td>\n",
" <td>W</td>\n",
" <td>Millions of U.S. Dollars</td>\n",
" <td>Mil. of U.S. $</td>\n",
" <td>Not Seasonally Adjusted</td>\n",
" <td>NSA</td>\n",
" <td>2024-08-15 15:37:22-05:00</td>\n",
" <td>94</td>\n",
" <td>94</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>H41RESPPALDKNWW</td>\n",
" <td>Assets: Liquidity and Credit Facilities: Loans...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2024-08-14</td>\n",
" <td>Weekly</td>\n",
" <td>W</td>\n",
" <td>Millions of U.S. Dollars</td>\n",
" <td>Mil. of U.S. $</td>\n",
" <td>Not Seasonally Adjusted</td>\n",
" <td>NSA</td>\n",
" <td>2024-08-15 15:37:01-05:00</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>TREAST</td>\n",
" <td>Assets: Securities Held Outright: U.S. Treasur...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2024-08-14</td>\n",
" <td>Weekly, As of Wednesday</td>\n",
" <td>W</td>\n",
" <td>Millions of U.S. Dollars</td>\n",
" <td>Mil. of U.S. $</td>\n",
" <td>Not Seasonally Adjusted</td>\n",
" <td>NSA</td>\n",
" <td>2024-08-15 15:36:54-05:00</td>\n",
" <td>71</td>\n",
" <td>71</td>\n",
" <td>The total face value of U.S. Treasury securiti...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" series_id title \\\n",
"0 WALCL Assets: Total Assets: Total Assets (Less Elimi... \n",
"1 H41RESPPALDKNWW Assets: Liquidity and Credit Facilities: Loans... \n",
"2 TREAST Assets: Securities Held Outright: U.S. Treasur... \n",
"\n",
" observation_start observation_end frequency frequency_short \\\n",
"0 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n",
"1 2002-12-18 2024-08-14 Weekly W \n",
"2 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n",
"\n",
" units units_short seasonal_adjustment \\\n",
"0 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n",
"1 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n",
"2 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n",
"\n",
" seasonal_adjustment_short last_updated popularity \\\n",
"0 NSA 2024-08-15 15:37:22-05:00 94 \n",
"1 NSA 2024-08-15 15:37:01-05:00 76 \n",
"2 NSA 2024-08-15 15:36:54-05:00 71 \n",
"\n",
" group_popularity notes \n",
"0 94 NaN \n",
"1 76 NaN \n",
"2 71 The total face value of U.S. Treasury securiti... "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The first result is the series we are looking for as the starting value.\n",
"\n",
"obb.economy.fred_search(\"Wednesday Levels\").to_df().head(3)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" <th>frequency</th>\n",
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" <th>notes</th>\n",
" <th>popularity</th>\n",
" <th>group_popularity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>WLRRAL</td>\n",
" <td>Liabilities and Capital: Liabilities: Reverse ...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2024-08-14</td>\n",
" <td>Weekly, As of Wednesday</td>\n",
" <td>W</td>\n",
" <td>Millions of U.S. Dollars</td>\n",
" <td>Mil. of U.S. $</td>\n",
" <td>Not Seasonally Adjusted</td>\n",
" <td>NSA</td>\n",
" <td>2024-08-15 15:37:49-05:00</td>\n",
" <td>Reverse repurchase agreements are transactions...</td>\n",
" <td>63</td>\n",
" <td>63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>WLRRAFOIAL</td>\n",
" <td>Liabilities and Capital: Liabilities: Reverse ...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2024-08-14</td>\n",
" <td>Weekly, As of Wednesday</td>\n",
" <td>W</td>\n",
" <td>Millions of U.S. Dollars</td>\n",
" <td>Mil. of U.S. $</td>\n",
" <td>Not Seasonally Adjusted</td>\n",
" <td>NSA</td>\n",
" <td>2024-08-15 15:37:36-05:00</td>\n",
" <td>Reverse repurchase agreements are transactions...</td>\n",
" <td>40</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>WLRRAOL</td>\n",
" <td>Liabilities and Capital: Liabilities: Reverse ...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2024-08-14</td>\n",
" <td>Weekly, As of Wednesday</td>\n",
" <td>W</td>\n",
" <td>Millions of U.S. Dollars</td>\n",
" <td>Mil. of U.S. $</td>\n",
" <td>Not Seasonally Adjusted</td>\n",
" <td>NSA</td>\n",
" <td>2024-08-15 15:37:40-05:00</td>\n",
" <td>NaN</td>\n",
" <td>29</td>\n",
" <td>29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" series_id title \\\n",
"0 WLRRAL Liabilities and Capital: Liabilities: Reverse ... \n",
"1 WLRRAFOIAL Liabilities and Capital: Liabilities: Reverse ... \n",
"2 WLRRAOL Liabilities and Capital: Liabilities: Reverse ... \n",
"\n",
" observation_start observation_end frequency frequency_short \\\n",
"0 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n",
"1 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n",
"2 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n",
"\n",
" units units_short seasonal_adjustment \\\n",
"0 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n",
"1 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n",
"2 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n",
"\n",
" seasonal_adjustment_short last_updated \\\n",
"0 NSA 2024-08-15 15:37:49-05:00 \n",
"1 NSA 2024-08-15 15:37:36-05:00 \n",
"2 NSA 2024-08-15 15:37:40-05:00 \n",
"\n",
" notes popularity \\\n",
"0 Reverse repurchase agreements are transactions... 63 \n",
"1 Reverse repurchase agreements are transactions... 40 \n",
"2 NaN 29 \n",
"\n",
" group_popularity \n",
"0 63 \n",
"1 40 \n",
"2 29 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Adding \"Reverse Repo\" to the search returns the second series in the equation, as the first result.\n",
"\n",
"obb.economy.fred_search(\"Wednesday Levels Reverse Repo\").to_df().head(3)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <th>observation_start</th>\n",
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" <th>frequency</th>\n",
" <th>frequency_short</th>\n",
" <th>units</th>\n",
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" <th>seasonal_adjustment_short</th>\n",
" <th>last_updated</th>\n",
" <th>notes</th>\n",
" <th>popularity</th>\n",
" <th>group_popularity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>WDTGAL</td>\n",
" <td>Liabilities and Capital: Liabilities: Deposits...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2024-08-14</td>\n",
" <td>Weekly, As of Wednesday</td>\n",
" <td>W</td>\n",
" <td>Millions of U.S. Dollars</td>\n",
" <td>Mil. of U.S. $</td>\n",
" <td>Not Seasonally Adjusted</td>\n",
" <td>NSA</td>\n",
" <td>2024-08-15 15:38:33-05:00</td>\n",
" <td>This account is the primary operational accoun...</td>\n",
" <td>64</td>\n",
" <td>64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>D2WLTGAL</td>\n",
" <td>Liabilities and Capital: Liabilities: Deposits...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2024-08-14</td>\n",
" <td>Weekly, As of Wednesday</td>\n",
" <td>W</td>\n",
" <td>Millions of U.S. Dollars</td>\n",
" <td>Mil. of U.S. $</td>\n",
" <td>Not Seasonally Adjusted</td>\n",
" <td>NSA</td>\n",
" <td>2024-08-15 15:38:37-05:00</td>\n",
" <td>NaN</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>WLDLCL</td>\n",
" <td>Liabilities and Capital: Liabilities: Deposits...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2024-08-14</td>\n",
" <td>Weekly, As of Wednesday</td>\n",
" <td>W</td>\n",
" <td>Millions of U.S. Dollars</td>\n",
" <td>Mil. of U.S. $</td>\n",
" <td>Not Seasonally Adjusted</td>\n",
" <td>NSA</td>\n",
" <td>2024-08-15 15:36:57-05:00</td>\n",
" <td>This item is the sum of \"Term deposits held by...</td>\n",
" <td>27</td>\n",
" <td>27</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" series_id title \\\n",
"0 WDTGAL Liabilities and Capital: Liabilities: Deposits... \n",
"1 D2WLTGAL Liabilities and Capital: Liabilities: Deposits... \n",
"2 WLDLCL Liabilities and Capital: Liabilities: Deposits... \n",
"\n",
" observation_start observation_end frequency frequency_short \\\n",
"0 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n",
"1 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n",
"2 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n",
"\n",
" units units_short seasonal_adjustment \\\n",
"0 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n",
"1 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n",
"2 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n",
"\n",
" seasonal_adjustment_short last_updated \\\n",
"0 NSA 2024-08-15 15:38:33-05:00 \n",
"1 NSA 2024-08-15 15:38:37-05:00 \n",
"2 NSA 2024-08-15 15:36:57-05:00 \n",
"\n",
" notes popularity \\\n",
"0 This account is the primary operational accoun... 64 \n",
"1 NaN 60 \n",
"2 This item is the sum of \"Term deposits held by... 27 \n",
"\n",
" group_popularity \n",
"0 64 \n",
"1 60 \n",
"2 27 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Refining the search for the Treasury General Account, returns the final series in the equation, as the first result.\n",
"\n",
"obb.economy.fred_search(\"Wednesday Levels Treasury General\").to_df().head(3)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>series_id</th>\n",
" <th>title</th>\n",
" <th>observation_start</th>\n",
" <th>observation_end</th>\n",
" <th>frequency</th>\n",
" <th>frequency_short</th>\n",
" <th>units</th>\n",
" <th>units_short</th>\n",
" <th>seasonal_adjustment</th>\n",
" <th>seasonal_adjustment_short</th>\n",
" <th>last_updated</th>\n",
" <th>notes</th>\n",
" <th>popularity</th>\n",
" <th>group_popularity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>SP500</td>\n",
" <td>S&amp;P 500</td>\n",
" <td>2014-08-22</td>\n",
" <td>2024-08-21</td>\n",
" <td>Daily, Close</td>\n",
" <td>D</td>\n",
" <td>Index</td>\n",
" <td>Index</td>\n",
" <td>Not Seasonally Adjusted</td>\n",
" <td>NSA</td>\n",
" <td>2024-08-21 19:21:03-05:00</td>\n",
" <td>The observations for the S&amp;P 500 represent the...</td>\n",
" <td>83</td>\n",
" <td>83</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" series_id title observation_start observation_end frequency \\\n",
"0 SP500 S&P 500 2014-08-22 2024-08-21 Daily, Close \n",
"\n",
" frequency_short units units_short seasonal_adjustment \\\n",
"0 D Index Index Not Seasonally Adjusted \n",
"\n",
" seasonal_adjustment_short last_updated \\\n",
"0 NSA 2024-08-21 19:21:03-05:00 \n",
"\n",
" notes popularity \\\n",
"0 The observations for the S&P 500 represent the... 83 \n",
"\n",
" group_popularity \n",
"0 83 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Several major equity indices are published to FRED, S&P 500 is one of them.\n",
"\n",
"obb.economy.fred_search(\"SP500\").to_df().head(2)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"By looking at the descriptions, we can confirm that all three Federal Reserve series are numbers as `Millions of USD`. If they were not all equivalent, some adjustments would need to be made before applying the equation."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'WALCL: Millions of U.S. Dollars'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'WLRRAL: Millions of U.S. Dollars'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'WDTGAL: Millions of U.S. Dollars'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'SP500: Index'"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for id in metadata:\n",
" display(f\"{id}: {metadata[id]['units']}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Inspecting the time series element shows that the S&P 500 data (as published to FRED) does not extend as far back as the others. Let's drop the NaN values and start the time series at a common starting point, which is approximately ten years ago."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>WALCL</th>\n",
" <th>WLRRAL</th>\n",
" <th>WDTGAL</th>\n",
" <th>SP500</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2002-12-18</th>\n",
" <td>719542.0</td>\n",
" <td>21905.0</td>\n",
" <td>6595.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-12-25</th>\n",
" <td>732059.0</td>\n",
" <td>20396.0</td>\n",
" <td>4662.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-01-01</th>\n",
" <td>730994.0</td>\n",
" <td>21091.0</td>\n",
" <td>4420.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-01-08</th>\n",
" <td>723762.0</td>\n",
" <td>18709.0</td>\n",
" <td>5490.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WALCL WLRRAL WDTGAL SP500\n",
"date \n",
"2002-12-18 719542.0 21905.0 6595.0 NaN\n",
"2002-12-25 732059.0 20396.0 4662.0 NaN\n",
"2003-01-01 730994.0 21091.0 4420.0 NaN\n",
"2003-01-08 723762.0 18709.0 5490.0 NaN"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>WALCL</th>\n",
" <th>WLRRAL</th>\n",
" <th>WDTGAL</th>\n",
" <th>SP500</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-08-27</th>\n",
" <td>4413736.0</td>\n",
" <td>282002.0</td>\n",
" <td>29547.0</td>\n",
" <td>2000.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-09-03</th>\n",
" <td>4415587.0</td>\n",
" <td>250306.0</td>\n",
" <td>21036.0</td>\n",
" <td>2000.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-09-10</th>\n",
" <td>4421408.0</td>\n",
" <td>267602.0</td>\n",
" <td>31872.0</td>\n",
" <td>1995.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-09-17</th>\n",
" <td>4449588.0</td>\n",
" <td>252224.0</td>\n",
" <td>123965.0</td>\n",
" <td>2001.57</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WALCL WLRRAL WDTGAL SP500\n",
"date \n",
"2014-08-27 4413736.0 282002.0 29547.0 2000.12\n",
"2014-09-03 4415587.0 250306.0 21036.0 2000.72\n",
"2014-09-10 4421408.0 267602.0 31872.0 1995.69\n",
"2014-09-17 4449588.0 252224.0 123965.0 2001.57"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(data.to_df().head(4))\n",
"display(data.to_df().dropna().head(4))\n",
"\n",
"# We'll create a new DataFrame object with the dropped rows.\n",
"liquidity_index = DataFrame(data.to_df().dropna())"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Applying the formula will simply be a matter of subtracting the first, three, columns."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>WALCL</th>\n",
" <th>WLRRAL</th>\n",
" <th>WDTGAL</th>\n",
" <th>SP500</th>\n",
" <th>USD Liquidity Index</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2024-07-24</th>\n",
" <td>7205455.0</td>\n",
" <td>805967.0</td>\n",
" <td>767419.0</td>\n",
" <td>5427.13</td>\n",
" <td>5632069.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-07-31</th>\n",
" <td>7178391.0</td>\n",
" <td>813261.0</td>\n",
" <td>854001.0</td>\n",
" <td>5522.30</td>\n",
" <td>5511129.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-08-07</th>\n",
" <td>7175256.0</td>\n",
" <td>681881.0</td>\n",
" <td>785233.0</td>\n",
" <td>5199.50</td>\n",
" <td>5708142.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-08-14</th>\n",
" <td>7177688.0</td>\n",
" <td>722198.0</td>\n",
" <td>788823.0</td>\n",
" <td>5455.21</td>\n",
" <td>5666667.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WALCL WLRRAL WDTGAL SP500 USD Liquidity Index\n",
"date \n",
"2024-07-24 7205455.0 805967.0 767419.0 5427.13 5632069.0\n",
"2024-07-31 7178391.0 813261.0 854001.0 5522.30 5511129.0\n",
"2024-08-07 7175256.0 681881.0 785233.0 5199.50 5708142.0\n",
"2024-08-14 7177688.0 722198.0 788823.0 5455.21 5666667.0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"liquidity_index[\"USD Liquidity Index\"] = (\n",
" liquidity_index[\"WALCL\"] - liquidity_index[\"WLRRAL\"] - liquidity_index[\"WDTGAL\"]\n",
")\n",
"\n",
"liquidity_index.tail(4)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that there are two items to compare, let's draw it!"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
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"x": 0.5,
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"source": [
"import plotly.graph_objects as go\n",
"\n",
"fig = go.Figure()\n",
"\n",
"fig.add_scatter(\n",
" x=liquidity_index.index,\n",
" y=liquidity_index[\"USD Liquidity Index\"] / 1000,\n",
" name=\"USD Liquidity Index (Billions)\",\n",
" yaxis=\"y1\",\n",
")\n",
"\n",
"fig.add_scatter(\n",
" x=liquidity_index.index,\n",
" y=liquidity_index[\"SP500\"],\n",
" name=\"S&P 500 Index\",\n",
" yaxis=\"y2\",\n",
")\n",
"\n",
"fig.update_layout(\n",
" yaxis=dict(\n",
" title=\"USD Liquidity Index (Billions)\",\n",
" side=\"left\",\n",
" position=0,\n",
" titlefont=dict(size=12),\n",
" showgrid=False,\n",
" ),\n",
" yaxis2=dict(\n",
" title=\"S&P 500 Index\",\n",
" side=\"right\",\n",
" overlaying=\"y\",\n",
" position=1,\n",
" titlefont=dict(size=12),\n",
" ),\n",
" title=\"USD Liquidity Index vs. S&P 500 Index\",\n",
" title_y=0.90,\n",
" title_x=0.5,\n",
" autosize=True,\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"To draw them both on the same y-axis, they will need to be normalized. There are several methods for normalizing a series, the fourth function in the block below paramaterizes a few of them, making it easy to A/B them."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
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" <tr style=\"text-align: right;\">\n",
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" <th>USD Liquidity Index</th>\n",
" <th>SP500</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
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" <th>2024-07-31</th>\n",
" <td>0.643578</td>\n",
" <td>0.970490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-08-07</th>\n",
" <td>0.698835</td>\n",
" <td>0.885139</td>\n",
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" <th>2024-08-14</th>\n",
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"text/plain": [
" USD Liquidity Index SP500\n",
"date \n",
"2024-07-31 0.643578 0.970490\n",
"2024-08-07 0.698835 0.885139\n",
"2024-08-14 0.687202 0.952750"
]
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"metadata": {},
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],
"source": [
"y_axis = liquidity_index[[\"USD Liquidity Index\", \"SP500\"]]\n",
"\n",
"\n",
"def absolute_maximum_scale(series):\n",
" return series / series.abs().max()\n",
"\n",
"\n",
"def min_max_scaling(series):\n",
" return (series - series.min()) / (series.max() - series.min())\n",
"\n",
"\n",
"def z_score_standardization(series):\n",
" return (series - series.mean()) / series.std()\n",
"\n",
"\n",
"methods = {\n",
" \"z\": z_score_standardization,\n",
" \"m\": min_max_scaling,\n",
" \"a\": absolute_maximum_scale,\n",
"}\n",
"\n",
"\n",
"def normalize(data: DataFrame, method: str = \"z\") -> DataFrame:\n",
" for col in data.columns:\n",
" data.loc[:, col] = methods[f\"{method}\"](data.loc[:, col])\n",
"\n",
" return data\n",
"\n",
"\n",
"normalized = normalize(y_axis, method=\"m\")\n",
"\n",
"normalized.tail(3)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Now they can be easily plotted using the built-in `DataFrame.plot` method."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
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"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "USD Liquidity Index vs. S&P 500 Index (Normalized)",
"x": 0.5,
"y": 0.9
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = go.Figure()\n",
"\n",
"fig.add_scatter(\n",
" x=normalized.index, y=normalized[\"USD Liquidity Index\"], name=\"USD Liquidity Index\"\n",
")\n",
"\n",
"fig.add_scatter(x=normalized.index, y=normalized[\"SP500\"], name=\"S&P 500 Index\")\n",
"\n",
"fig.update_layout(\n",
" title=\"USD Liquidity Index vs. S&P 500 Index (Normalized)\",\n",
" title_y=0.90,\n",
" title_x=0.5,\n",
" autosize=True,\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"The combinations are endless and we love seeing your creations, tag us on social media with your custom indexes and indicators."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "obb",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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